March 2025 arXiv papers — page 119

Jian Cui, Pu Zhang

In contrast with the Hovey correspondence of abelian model structures from two compatible complete cotorsion pairs, Beligiannis and Reiten give a construction of model structures on abelian categories from one hereditary complete cotorsion pair. The aim of this paper is to extend this result to triangulated categories together with a proper class $\xi$ of tr

Sebastian Reich

We consider the problem of optimal control for partially observed dynamical systems. Despite its prevalence in practical applications, there are still very few algorithms available, which take uncertainties in the current state estimates and future observations into account. In other words, most current approaches separate state estimation from the optimal c

Manting Peng, Kailiang Wu, Caiyou Yuan

This paper proposes and analyzes a class of essentially non-oscillatory central discontinuous Galerkin (CDG) methods for general hyperbolic conservation laws. First, we introduce a novel compact, non-oscillatory stabilization mechanism that effectively suppresses spurious oscillations while preserving the high-order accuracy of CDG methods. Unlike existing l

Wenbo Dai, Lijing Lu, Zhihang Li

The performance of models is intricately linked to the abundance of training data. In Visible-Infrared person Re-IDentification (VI-ReID) tasks, collecting and annotating large-scale images of each individual under various cameras and modalities is tedious, time-expensive, costly and must comply with data protection laws, posing a severe challenge in meeting

Felix Otto, Matteo Palmieri, Christian Wagner

We study a $(1+1)$-dimensional semi-discrete random variational problem that can be interpreted as the geometrically linearized version of the critical $2$-dimensional random field Ising model. The scaling of the correlation length of the latter was recently characterized in [12] and [13, Section 5]; our analysis is reminiscent of the multi-scale approach of

Han Mei, Kunqian Li, Shuaixin Liu, Chengzhi Ma

Due to the complex interplay of light absorption and scattering in the underwater environment, underwater images experience significant degradation. This research presents a two-stage underwater image enhancement network called the Data-Driven and Physical Parameters Fusion Network (DPF-Net), which harnesses the robustness of physical imaging models alongsid

Marcus Johan Schytt, Halldór Gauti Pétursson, John Bagterp Jørgensen

This paper presents a hybrid optimization methodology for parameter estimation of reactive transport systems. Using reduced-order advection-diffusion-reaction (ADR) models, the computational requirements of global optimization with dynamic PDE constraints are addressed by combining metaheuristics with gradient-based optimizers. A case study in preparative li

Ruitao Xiao, Yingze Su, Junnian Xiong, Hui Li

In theoretical studies of two-dimensional (2D) systems, the Mermin-Wagner theorem prevents continuous symmetry breaking at any finite temperature, thus forbidding a Landau phase transition at a critical temperature $T_c$. The difficulty arises when many-body theoretical studies predict a Landau phase transition at finite temperatures, which contradicts the M

Bo Liu, Wei Wang, Charles Moulinec, Stefano Rolfo

The aim of this work is to further expand the capability of the coarse-grid Computational Fluid Dynamics (CFD) approach, SubChCFD, to effectively simulate transient and buoyancy-influenced flows, which are critical in accident analyses of High-Temperature Gas-cooled Reactors (HTGRs). It has been demonstrated in our previous work that SubChCFD is highly adapt

Jiahang Cao, Qiang Zhang, Hanzhong Guo, Jiaxu Wang

Diffusion Policy (DP) has attracted significant attention as an effective method for policy representation due to its capacity to model multi-distribution dynamics. However, current DPs are often based on a single visual modality (e.g., RGB or point cloud), limiting their accuracy and generalization potential. Although training a generalized DP capable of ha

Shumo Cui, Kailiang Wu, Linfeng Xu

This paper establishes the minimum entropy principle (MEP) for the relativistic Euler equations with a broad class of equations of state (EOSs) and addresses the challenge of preserving the local version of the discovered MEP in high-order numerical schemes. At the continuous level, we find out a family of entropy pairs for the relativistic Euler equations a

Alessio Xompero, Andrea Cavallaro

Subjective interpretation and content diversity make predicting whether an image is private or public a challenging task. Graph neural networks combined with convolutional neural networks (CNNs), which consist of 14,000 to 500 millions parameters, generate features for visual entities (e.g., scene and object types) and identify the entities that contribute t

Frederic Bippus, Juraj Krsnik, Motoharu Kitatani, Luka Akšamović

We find a strongly enhanced entanglement within the pseudogap regime of the Hubbard model. This entanglement is estimated from the quantum Fisher information and, avoiding the ill-conditioned analytical continuation, the quantum variance. Both are lower bounds for the actual entanglement that can be calculated from the (antiferromagnetic) susceptibility, obt

Vu Tuan Hai

Machine learning has been widely applied in many aspects, but training a machine learning model is increasingly difficult. There are more optimization problems named "black-box" where the relationship between model parameters and outcomes is uncertain or complex to trace. Currently, optimizing black-box models that need a large number of query observations a

A. M. Tatarnikov, A. A. Tatarnikova, N. A. Maslennikova, A. V. Dodin

Just less than 300 symbiotic stars are currently known in the Galaxy. The population synthesis methods predict that this amount should be 10--100 times larger. In recent years, several works have attempted to find symbiotic candidates from photometric surveys. Regular spectroscopic observations of these candidates can increase the number of known symbiotic s

Fanhu Zeng, Hao Tang, Yihua Shao, Siyu Chen

A high-performance image compression algorithm is crucial for real-time information transmission across numerous fields. Despite rapid progress in image compression, computational inefficiency and poor redundancy modeling still pose significant bottlenecks, limiting practical applications. Inspired by the effectiveness of state space models (SSMs) in capturi

Tobias Hallmen, Robin-Nico Kampa, Fabian Deuser, Norbert Oswald

In this study, we present our methodology for two tasks: the Emotional Mimicry Intensity (EMI) Estimation Challenge and the Behavioural Ambivalence/Hesitancy (BAH) Recognition Challenge, both conducted as part of the 8th Workshop and Competition on Affective & Behavior Analysis in-the-wild. We utilize a Wav2Vec 2.0 model pre-trained on a large podcast datase

Zhicheng Wang, Zhiyu Pan, Zhan Peng, Jian Cheng

Referring expression counting (REC) algorithms are for more flexible and interactive counting ability across varied fine-grained text expressions. However, the requirement for fine-grained attribute understanding poses challenges for prior arts, as they struggle to accurately align attribute information with correct visual patterns. Given the proven importan

Kazunori Fujisawa, Bruno R. Carvalho, Pedro Venezuela, Cheon-Soo Kang

Point defects, though atomically small, significantly influence the properties of 2D materials. A general method for characterizing point defect density ($n_{ D }$) in graphenic materials with arbitrary layer number ($n_{ L }$) is currently lacking. Here, we introduce the Graphene Atlas, a non-destructive Raman spectroscopy-based framework for defect quantif

L. Scharenberg, J. Alozy, W. Billereau, F. Brunbauer

The combination of Micro-Pattern Gaseous Detectors (MPGDs) and pixel charge readout enables specific experimental opportunities. Using the Timepix4 for the readout is advantageous because of its size (around 7 cm^2 active area) and its Through Silicon Vias. The latter enables to connect to the Timepix4 from the back side. Thus, it can be tiled on four sides,

Neelanga Thelasingha, Agung Julius, James Humann, James Dotterweich

In complex multi-agent systems involving heterogeneous teams, uncertainty arises from numerous sources like environmental disturbances, model inaccuracies, and changing tasks. This causes planned trajectories to become infeasible, requiring replanning. Further, different communication architectures used in multi-agent systems give rise to asymmetric knowledg

Ke Chen, Dandan Jiang

The process generates substantial amounts of data with highly complex structures, leading to the development of numerous nonlinear statistical methods. However, most of these methods rely on computations involving large-scale dense kernel matrices. This dependence poses significant challenges in meeting the high computational demands and real-time responsive

Nobuhito Maru, Ryujiro Nago

We propose a simple model of family unification, which is a six dimensional $SO(20)$ gauge theory with a single fermion in the spinorial representation. After compactification to five dimensions, our model gives a five dimensional model where the Standard Model Higgs field is unified into the fifth component of the five dimensional gauge field as well as thr

Sean Xiao, Sangwoo Park, Stefan Vlaski

Stochastic first-order methods for empirical risk minimization employ gradient approximations based on sampled data in lieu of exact gradients. Such constructions introduce noise into the learning dynamics, which can be corrected through variance-reduction techniques. There is increasing evidence in the literature that in many modern learning applications no

Edgar Heinert, Thomas Gottwald, Annika Mütze, Matthias Rottmann

Previous works studied how deep neural networks (DNNs) perceive image content in terms of their biases towards different image cues, such as texture and shape. Previous methods to measure shape and texture biases are typically style-transfer-based and limited to DNNs for image classification. In this work, we provide a new evaluation procedure consisting of

G. Waratkar, M. Dixit, S. P. Tendulkar, V. Bhalerao

Fast Radio Bursts (FRBs) are short-duration, highly-energetic extragalactic radio transients with unclear origins & emission mechanisms. Despite extensive multi-wavelength searches, no credible X-ray or other prompt electromagnetic counterparts have been found for extragalactic FRBs. We present results from a comprehensive search for such prompt X-ray counte

Sangwoo Park, Stefan Vlaski, Lajos Hanzo

In multi-objective optimization, minimizing the worst objective can be preferable to minimizing the average objective, as this ensures improved fairness across objectives. Due to the non-smooth nature of the resultant min-max optimization problem, classical subgradient-based approaches typically exhibit slow convergence. Motivated by primal-dual consensus te

Juhyeong Kim, Sungyoon Choi, Youngbin Lee, Yejin Kim

We propose Decision by Supervised Learning (DSL), a practical framework for robust portfolio optimization. DSL reframes portfolio construction as a supervised learning problem: models are trained to predict optimal portfolio weights, using cross-entropy loss and portfolios constructed by maximizing the Sharpe or Sortino ratio. To further enhance stability an

Hossein Ranjbar, Alireza Taheri

Sign language recognition involves modeling complex multichannel information, such as hand shapes and movements while relying on sufficient sign language-specific data. However, sign languages are often under-resourced, posing a significant challenge for research and development in this field. To address this gap, we introduce ISLR101, the first publicly ava

Feihong Yan, Qingyan Wei, Jiayi Tang, Jiajun Li

Masked Autoregressive (MAR) models have emerged as a promising approach in image generation, expected to surpass traditional autoregressive models in computational efficiency by leveraging the capability of parallel decoding. However, their dependence on bidirectional self-attention inherently conflicts with conventional KV caching mechanisms, creating unexp

Yuxin Chen, Peng Tang, Weidong Qiu, Shujun Li

Privacy policies are widely used by digital services and often required for legal purposes. Many machine learning based classifiers have been developed to automate detection of different concepts in a given privacy policy, which can help facilitate other automated tasks such as producing a more reader-friendly summary and detecting legal compliance issues. D

Kai Xiao, Yang Huang, Haibo Yuan, Zhirui Li

We present a pioneering achievement in the high-precision photometric calibration of CMOS-based photometry, by application of the Gaia BP/RP (XP) spectra-based synthetic photometry (XPSP) method to the mini-SiTian array (MST) photometry. Through 79 repeated observations of the $\texttt{f02}$ field on the night, we find good internal consistency in the calibr

Matti Lassas

We consider inverse problems for non-linear hyperbolic and elliptic equations and give an introduction to the method based on the multiple linearization, or on the construction of artificial sources, to solve these problems. The method is based on self-interaction of linearized waves or other solutions in the presence of non-linearities. Multiple linearizati

Li Yicong

After a decade of prosperity, the development of video understanding has reached a critical juncture, where the sole reliance on massive data and complex architectures is no longer a one-size-fits-all solution to all situations. The presence of ubiquitous data imbalance hampers DNNs from effectively learning the underlying causal mechanisms, leading to signi

Tianle Li, Yongming Rao, Winston Hu, Yu Cheng

Encoder-free multimodal large language models(MLLMs) eliminate the need for a well-trained vision encoder by directly processing image tokens before the language model. While this approach reduces computational overhead and model complexity, it often requires large amounts of training data to effectively capture the visual knowledge typically encoded by visi

Jaroslaw Wojtun, Cezary Ziolkowski, Jan M. Kelner, Tomas Mikulasek

Vegetation significantly affects radio signal attenuation, influenced by factors such as signal frequency, plant species, and foliage density. Existing attenuation models typically address specific scenarios, like single trees, rows of trees, or green spaces, with the ITU-R P.833 recommendation being a widely recognized standard. Most assessments for single

Hendrik Hadenfeldt, Jonas Arlt, Tobias Meyer, Felix Junge

The BeEST experiment is measuring the ${}^{7}$Li recoil spectrum from the decay of ${}^{7}$Be implanted into Ta-based sensors to provide the most stringent limits on the existence of sterile neutrinos in the sub-MeV mass range. Its sensitivity is limited by spectral broadening due to interactions between the atomic shell of the ${}^{7}$Be/${}^{7}$Li and the

Jan M. Kelner, Cezary Ziolkowski, Michal Kryk, Jaroslaw Wojtun

In this paper, we present an empirical verification of the method of determining the Doppler spectrum (DS) from the power angular spectrum (PAS). Measurements were made for the frequency of 3.5 GHz, under non-line-of-sight conditions in suburban areas characteristic of a university campus. In the static scenario, the measured PAS was the basis for the determ

Jaroslaw Wojtun, Cezary Ziolkowski, Jan M. Kelner, Aniruddha Chandra

In this paper, we analyze the spectral efficiency for millimeter wave downlink with beam misalignment in urban macro scenario. For this purpose, we use a new approach based on the modified Shannon formula, which considers the propagation environment and antenna system coefficients. These factors are determined based on a multi-ellipsoidal propagation model.

Yuda Zou, Zelong Liu, Yuliang Gu, Bo Du

Crowd counting and localization are important in applications such as public security and traffic management. Existing methods have achieved impressive results thanks to extensive laborious annotations. This paper propose a novel point-localization-based semi-supervised crowd counting and localization method termed Consistent-Point. We identify and address t

Tsz Chung Cheng, Chung Shing Cheng, Chaak Ming Lau, Eugene Tin-Ho Lam

The ability of language models to comprehend and interact in diverse linguistic and cultural landscapes is crucial. The Cantonese language used in Hong Kong presents unique challenges for natural language processing due to its rich cultural nuances and lack of dedicated evaluation datasets. The HKCanto-Eval benchmark addresses this gap by evaluating the perf

Xuan Mao, Meng Liu, Yuxiang Li

This paper is concerned with a parabolic-parabolic-parabolic chemotaxis system with indirect signal production, modelling the impact of phenotypic heterogeneity on population aggregation \begin{equation*} \begin{cases} u_t = \Delta u - \nabla\cdot(u\nabla v),\\ v_t = \Delta v - v + w,\\ w_t = \Delta w - w + u, \end{cases} \end{equation*} posed on a ball in $

Wei Chen, Shutao Zhang, Chongwu Wang, Yiming Wu

Silicon photodetectors are highly desirable for their CMOS compatibility, low cost, and fast response speed. However, their application the infrared (IR) is limited by silicon's intrinsic bandgap, which restricts its detection to photons with wavelengths shorter than 1100 nm. Although several methods have been developed to extend silicon photodetectors furth

Zhiyuan Xi, Kun Zhu, Yuanyuan Xu, Tong Zhang

Encoder, decoder and knowledge base are three major components for semantic communication. Recent advances have achieved significant progress in the encoder-decoder design. However, there remains a considerable gap in the construction and utilization of knowledge base, which plays important roles in establishing consensus among communication participants thr

Hai Dang, Chelse Swoopes, Daniel Buschek, Elena L. Glassman

Many communities, including the scientific community, develop implicit writing norms. Understanding them is crucial for effective communication with that community. Writers gradually develop an implicit understanding of norms by reading papers and receiving feedback on their writing. However, it is difficult to both externalize this knowledge and apply it to

Wei Nan, Bing Guo, Jie Chen, Baoqun Cui

The Beijing Radioactive Ion-beam Facility (BRIF), which is based on Isotope Separation On-Line (ISOL) technique, consists of a 100 MeV proton cyclotron as the driving accelerator, a two-stage ISOL system for ion separation, a 13-MV tandem accelerator for post-acceleration, a superconducting linac for further boosting beam energies. It is capable of providing

Martino Chiarani, Swastika Roy, Christos Verikoukis, Fabrizio Granelli

In recent years, network slicing has embraced artificial intelligence (AI) models to manage the growing complexity of communication networks. In such a situation, AI-driven zero-touch network automation should present a high degree of flexibility and viability, especially when deployed in live production networks. However, centralized controllers suffer from

Shangheng Du, Jiabao Zhao, Jinxin Shi, Zhentao Xie

With the rapid development of Large Language Models (LLMs), LLM-based agents have been widely adopted in various fields, becoming essential for autonomous decision-making and interactive tasks. However, current work typically relies on prompt design or fine-tuning strategies applied to vanilla LLMs, which often leads to limited effectiveness or suboptimal pe

Fernando De Terán, Bruno Iannazzo

We provide a characterization for a periodic system of generalized Sylvester and conjugate-Sylvester equations, with at most one generalized conjugate-Sylvester equation, to have a unique solution when all coefficient matrices are square and all unknown matrices of the system have the same size. We also present a procedure to reduce an arbitrary system of ge

Shuwen Chen, Fangyang Zheng

In a recent work, Kai Tang conjectured that any compact Hermitian manifold with non-zero constant mixed curvature must be K\"ahler. He confirmed the conjecture in complex dimension $2$ and for Chern K\"ahler-like manifolds in general dimensions. In this paper, we verify his conjecture for several special types of Hermitian manifolds, including complex nilman

Abhinav Sharma, Suhas B Mahesh, Anish Kumar, Karthik V Pai

The main goal of this paper is to obtain sufficient conditions so that Le Roy type functions and multivariate Le Roy type functions satisfy subordination of exponential function. Moreover conditions on parameters have been derived to claim them being exponential starlike and exponential convex for both of the functions. Starlikeness, convexity and close-to-c

Tanech Klangburam, Chakrit Pongkitivanichkul

We investigate the effects of the ALP-mediated dark matter (DM) model on neutron star properties using the Quantum Hadrodynamics model (QHD). Using the relativistic mean-field approximation with the QHD-ALP-DM framework, we compute the equation of state (EoS) of neutron stars. Based on our previous study, we find that typical ALP parameter values have no sig

Vicente Muñoz, Juan Rojo

In the breakthrough paper [V. Mu\~noz, A Smale-Barden manifold admitting K-contact but not Sasakian structure, 2024, 10.4171/JEMS/1496], it is constructed the first example of a simply connected compact 5-manifold (aka.\ Smale-Barden manifold) which admits a K-contact structure but does not carry a Sasakian structure, thus settling the question raised as Ope

Bocheng Wang, Chusheng Zeng, Mulin Chen, Xuelong Li

Deep multi-view clustering incorporating graph learning has presented tremendous potential. Most methods encounter costly square time consumption w.r.t. data size. Theoretically, anchor-based graph learning can alleviate this limitation, but related deep models mainly rely on manual discretization approaches to select anchors, which indicates that 1) the anc

George Theodorou, Stavros Komineas

We consider an antiferromagnet in one space dimension with easy-axis anisotropy in a perpendicular magnetic field. We study propagating domain wall solutions that can have a velocity up to a maximum $v_c$. The width of the domain wall is a non-monotonic function of the velocity and it diverges to infinity at $v_c$. Both features are in contrast to the case o

Erhard Reschenhofer

An attempt is made to estimate and forecast the trend of the global annual and monthly mean temperatures. The results of a conventional statistical analysis suggest that in the absence of unforeseeable events such as a sudden acceleration in the rate of warming, the 1.5{\deg}C Paris Agreement threshold could be exceeded between 2027 and 2031. However, carryi

Kaiyuan Wang, Qi Jie Wang, Matthew R. Foreman, Yu Luo

Exceptional points (EPs) in non-Hermitian photonic systems have attracted considerable research interest due to their singular eigenvalue topology and associated anomalous physical phenomena. These properties enable diverse applications ranging from enhanced quantum metrology to chiral light-matter interactions. Practical implementation of high order EPs in

Mohamed M. S. Nasser, Christopher C. Green, El Mostafa Kalmoun

We present a unified numerical method to determine the shapes of multiple Hele-Shaw bubbles in steady motion, and in the absence of surface tension, in three planar domains: free space, the upper half-plane, and an infinite channel. Our approach is based on solving the free boundary problem for the bubble boundaries using a fast and accurate boundary integra

Yuri Antonacci, Chiara Bara', Laura Sparacino, Gorana Mijatovic

Several data-driven approaches based on information theory have been proposed for analyzing high-order interactions involving three or more components of a network system. Most of these methods are defined only in the time domain and rely on the assumption of stationarity in the underlying dynamics, making them inherently unable to detect frequency-specific

Amit Kumar Singh

In this article, we study the smoothness of the moduli space of finite quiver vector bundles over the smooth complex projective curves.

Luming Wang, Hao Shi, Xiaoting Yin, Kailun Yang

Egocentric gesture recognition is a pivotal technology for enhancing natural human-computer interaction, yet traditional RGB-based solutions suffer from motion blur and illumination variations in dynamic scenarios. While event cameras show distinct advantages in handling high dynamic range with ultra-low power consumption, existing RGB-based architectures fa

Shuo Gao, Jingyang Zhang, Jun Xue, Meng Yang

Carotid atherosclerosis represents a significant health risk, with its early diagnosis primarily dependent on ultrasound-based assessments of carotid intima-media thickening. However, during carotid ultrasound screening, significant view variations cause style shifts, impairing content cues related to thickening, such as lumen anatomy, which introduces spuri

Orr Barnea, Dror Einav, Jonas Drotleff, Idan Hochner

Stray electric fields induce excess micromotion in ion traps, limiting experimental performance. We present a new micromotion-compensation technique that utilizes a dark ion in a bright-dark-bright linear ion crystal. Stray electric fields in the radial plane of the trap deform the crystal axially. We exploit the mode softening near the transition to the zig

Pak-Yeung Chan, Man-Chun Lee

In this work, we construct several sequences of metrics on sphere with different limiting behaviors. By combining with the work of Deruelle, we use it and the localized maximum principle to construct various examples of expanding gradient Ricci solitons with positive curvature and exotic curvature decay. This answers a question proposed by Chow-Lu-Ni and als

Xiaohui Li, Qi Zhu, Yunpei Chen, Chadi Assi

Integrated sensing and communication (ISAC) has the potential to facilitate coordination gains from mutual assistance between sensing and communication (S&C), especially sensing-aided communication enhancement (SACE). Reconfigurable intelligent surface (RIS) is another potential technique for achieving resource-efficient communication enhancement. Therefore,

Srdjan Petrovic, Nikola Starcevic, Nace Stojanov, Liang Huang

This study reports on the evolution of the probability distribution in the configuration space of the two-dimensional Toda system. The distribution is characterized by singularities, which predominantly take two forms: double-cusped triangular lines and lines parallel to the equipotential line that defines the accessible region. Over time, the number of thes

Ya-Bing Zuo, Jia-Yu Zou, Shi-Yu Liang, Ming-Ge Li

In this study, the nonleptonic two-body $B$ decays into two tensor mesons (including $a_2(1320)$, $K^*_2(1430)$, $f_2(1270)$, $f^\prime_2(1525)$, denoted generically as $T$) are investigated in the QCD factorization approach. The branching ratios, longitudinal polarization fractions, and CP asymmetries are predicted systematically. It is found that, from the

Lujia Tian, Lihui Han, Yuanfang Yue, Huazhen Li

Two-dimensional (2D) transition metal nitrides have a wide prospect of applications in the fields of physics, chemistry, materials, etc. However, 2D transition metal nitrides with strong magnetism, especially high N$\rm \acute{e}$el temperature, are very scarce. Based on the first-principles calculations within the framework of density functional theory, we

Frank Lechermann, Steffen Bötzel, Ilya M. Eremin

Developing a low-energy model is essential for understanding unconventional superconductivity in bilayer nickelate La$_3$Ni$_2$O$_7$. Here, we analyze distinct low-energy scenarios of the normal state by downfolding the ab-initio determined band structure and applying the mean-field regime of rotational-invariant slave-boson theory. We compare models based o

Zhaojun Xing

In this paper, we first give a critical BKM-type blow-up criterion that only involves the horizontal swirl component of the velocity for the inviscid axially symmetric MHD-Boussinesq system. Moreover, we consider the inviscid limit of the viscous MHD-Boussinesq system, and the convergence rate for the viscosity coefficient tending to zero is obtained.

Xinlin Zhao, Song Wang, Jifeng Liu

Compact objects undergoing mass transfer exhibit significant (and double-peaked) $H_{\alpha}$ emission lines. Recently, new methods have been developed to identify black hole X-ray binaries (BHXBs) and calculate their systematic parameters using $H_{\alpha}$ line parameters, such as the full-width at half maximum (FWHM), equivalent width (EW), and separation

Noboru Chikami, Masahiro Ikeda, Koichi Taniguchi, Slim Tayachi

We construct asymptotically self-similar global solutions to the Hardy-H\'enon parabolic equation $\partial_t u - \Delta u = \pm |x|^{\gamma} |u|^{\alpha-1} u$, $\alpha>1$, $\gamma \in \mathbb{R}$ for a large class of initial data belonging to weighted Lorentz spaces. The solution may be asymptotic to a self-similar solution of the linear heat equation or to

Kohsuke Shibata

We characterize a binomial such that the Artinian algebra whose Macaulay dual generator is the binomial is a complete intersection. As an application, we prove that the Artinian algebra with a binomial Macaulay dual generator has the strong Lefschetz property in characteristic 0 if the Artinian algebra is a complete intersection.

Binggwong Leung, Worasuchad Haomachai, Joachim Winther Pedersen, Sebastian Risi

Artificial neural networks can be used to solve a variety of robotic tasks. However, they risk failing catastrophically when faced with out-of-distribution (OOD) situations. Several approaches have employed a type of synaptic plasticity known as Hebbian learning that can dynamically adjust weights based on local neural activities. Research has shown that syn

Jie Dai, Yuchen Liu, Jiakang Zheng, Ruichen Zhang

In recent years, high-speed trains (HSTs) communications have developed rapidly to enhance the stability of train operations and improve passenger connectivity experiences. However, as the train continues to accelerate, urgent technological innovations are needed to overcome challenges such as frequency handover and significant Doppler effects. In this paper

Jianhao Yang, Wenshuo Yu, Yuanchao Lv, Jiance Sun

Remote sensing image segmentation is crucial for environmental monitoring, disaster assessment, and resource management, but its performance largely depends on the quality of the dataset. Although several high-quality datasets are broadly accessible, data scarcity remains for specialized tasks like marine oil spill segmentation. Such tasks still rely on manu

Mariantonia Cotronei, Woula Themistoclakis, Marc Van Barel

This paper investigates the potential applications of a parametric family of polynomial wavelets that has been recently introduced starting from de la Vall\'ee Poussin (VP) interpolation at Chebyshev nodes. Unlike classical wavelets, which are constructed on the real line, these VP wavelets are defined on a bounded interval, offering the advantage of handlin

Roger Züst

Building upon the construction of a Cayley calibration adapted to a complex structure, we introduce a calibration $Φ$ in $\bigwedge^8 \mathbf R^{16}$ with $|Φ^2| = 294$. This enables us to show that the product of two orthogonally supported calibrations is not necessarily a calibration, thereby providing a negative answer to a question posed by Federer. Dado

Jianwei Zhao, Xin Li, Fan Yang, Qiang Zhai

Whole Slide Image (WSI) classification poses unique challenges due to the vast image size and numerous non-informative regions, which introduce noise and cause data imbalance during feature aggregation. To address these issues, we propose MExD, an Expert-Infused Diffusion Model that combines the strengths of a Mixture-of-Experts (MoE) mechanism with a diffus

Mohammad Nafees, Dharmendra Dixit, Arvind Kumar

Backscatter communication is an energy-efficient technique that enables sustainable wireless connectivity with a minimal environmental impact. In this paper, the secrecy performance of practical non-linear energy-harvesting backscatter communications with various tag selection schemes is analyzed in Nakagami-m fading channels. We consider four tag selection

Jiangdong Cai, Yan Chen, Zhenrong Shen, Haotian Jiang

In digital pathology, acquiring all-in-focus images is essential to high-quality imaging and high-efficient clinical workflow. Traditional scanners achieve this by scanning at multiple focal planes of varying depths and then merging them, which is relatively slow and often struggles with complex tissue defocus. Recent prevailing image restoration technique p

Chong-Chung Lih, Chao-Qiang Geng

We investigate the exclusive semilpetonic decays of $\Lambda^{+}_{c}\to (\Lambda/n) \ell^{+} \nu_{\ell}~(\ell=e,\mu)$ within the standard model by using the light-front quark model (LFQM). The form factor behaviors are obtained from the effective treatment of nonvalence contributions in addition to the valence ones in the Drell-Yan-West frame due to the Beth

Woula Themistoclakis, Marc Van Barel

On a compact interval, we introduce and study a whole family of wavelets depending on a free parameter that can be suitably modulated to improve performance. Such wavelets arise from de la Vall\'ee Poussin (VP) interpolation at Chebyshev nodes, generalizing previous work by Capobianco and Themistoclakis who considered a special parameter setting. In our cons

Zongtang Wan, Yuqian Zhao, Xun Chen, Zhaohua Ma

The frustrated honeycomb spin model can stabilize a subextensively degenerate spiral spin liquid with nontrivial topological excitations and defects, but its material realization remains rare. Here, we report the experimental realization of this model in the structurally disorder-free compound GdZnPO. Using a single-crystal sample, we find that spin-7/2 rare

Heng Zhang, Guoxiang Zhao, Xiaoqiang Ren

Pursuit-evasion (PE) problem is a critical challenge in multi-robot systems (MRS). While reinforcement learning (RL) has shown its promise in addressing PE tasks, research has primarily focused on single-target pursuit, with limited exploration of multi-target encirclement, particularly in large-scale settings. This paper proposes a Transformer-Enhanced Rein

Fernando Rodriguez Avellaneda, Erick A. Chacón-Montalván, Paula Moraga

Air pollution remains a critical environmental and public health challenge, demanding high-resolution spatial data to better understand its spatial distribution and impacts. This study addresses the challenges of conducting multivariate spatial analysis of air pollutants observed at aggregated levels, particularly when the goal is to model the underlying con

Cheng-Qun Pang, Hao Chen, Yun-Hai Zhang

We conducted a study using the modified Godfrey-Isgur quark model and quark pair creation model to investigate the spectrum and two-body strong decays of the newly discovered $\kappa$(2600) resonance by the LHCb collaboration. Our analysis revealed that this {resonance} can be assigned as the fourth radial excitation within the $0^{+}$ light strange meson fa

Marek Kwiek

In this paper, we focus on a rare scholarly theme of highly productive academics, statistically confirming their pivotal role in knowledge production across 11 systems studied. The upper 10 % of highly productive academics in 11 European countries studied (N=17,211) provide on average almost half of all academic knowledge production. In contrast to dominatin

Artem Lensky

This study investigates EEG as a potential early biomarker by applying deep learning techniques to resting-state EEG recordings from 31 subjects (15 with PD and 16 healthy controls). EEG signals underwent preprocessing to remove tremor artifacts before classification with CNNs using wavelet-based electrode triplet images. Our analysis across different brain

Tao Hou, Huanyang Chen

As a lens capable of sending images of deep sub-wavelength objects to the far field, the hyperlens has garnered significant attention for its super-resolution and magnification capabilities. However, traditional hyperlenses require extreme permittivity ratios and fail to achieve geometrically perfect imaging, significantly constraining their practical applic

Z. Neishabouri, K. Azizi

We study the semileptonic decays of $\Xi^{(')}_{b}\rightarrow\Xi^{(')}_{c}{\ell}\bar\nu_{\ell}$ in all lepton channels. To do this, we first obtain the form factors defining these decay modes within the framework of QCD sum rules. Then, using the transferred momentum squared-dependent form factors, we compute the decay widths and branching fractions for all

Mingzhu Wu, Jianan Jiang, Xinglin Li, Hanhui Deng

Collaboration can amalgamate diverse ideas, styles, and visual elements, fostering creativity and innovation among different designers. In collaborative design, sketches play a pivotal role as a means of expressing design creativity. However, designers often tend to not openly share these meticulously crafted sketches. This phenomenon of data island in the d

Lester Phillip Violeta, Wen-Chin Huang, Tomoki Toda

We propose Serenade, a novel framework for the singing style conversion (SSC) task. Although singer identity conversion has made great strides in the previous years, converting the singing style of a singer has been an unexplored research area. We find three main challenges in SSC: modeling the target style, disentangling source style, and retaining the sour

Yanpeng Jia, Shiyi Wang, Shiliang Shao, Yue Wang

Ground robots play a crucial role in inspection, exploration, rescue, and other applications. In recent years, advancements in LiDAR technology have made sensors more accurate, lightweight, and cost-effective. Therefore, researchers increasingly integrate sensors, for SLAM studies, providing robust technical support for ground robots and expanding their appl

Kuan-Lin Chen, Bhaskar D. Rao

Covariance matrix reconstruction has been the most widely used guiding objective in gridless direction-of-arrival (DoA) estimation for sparse linear arrays. Many semidefinite programming (SDP)-based methods fall under this category. Although deep learning-based approaches enable the construction of more sophisticated objective functions, most methods still r

Yutao Hu, Sen Li, Jincheng Yan, Wenqi Shao

Fine-grained visual categorization (FGVC) is a challenging but significant task in computer vision, which aims to recognize different sub-categories of birds, cars, airplanes, etc. Among them, recognizing models of different cars has significant application value in autonomous driving, traffic surveillance and scene understanding, which has received consider

Yunrui Song, Chengbing Qin, Yuanyuan Li, Xiangdong Li

Superbunching effect with second-order correlations larger than 2, $g^{(2)}(0)>2$, indicating the N-photon bundles emission and strong correlation among photons, has a broad range of fascinating applications in quantum illumination, communication, and computation. However, the on-demand manipulation of the superbunching effect in colloidal quantum dots (QDs)

Songen Gu, Haoxuan Song, Binjie Liu, Qian Yu

We propose VRSketch2Gaussian, a first VR sketch-guided, multi-modal, native 3D object generation framework that incorporates a 3D Gaussian Splatting representation. As part of our work, we introduce VRSS, the first large-scale paired dataset containing VR sketches, text, images, and 3DGS, bridging the gap in multi-modal VR sketch-based generation. Our approa

Kang You, Tong Chen, Dandan Ding, M. Salman Asif

Despite the substantial advancements demonstrated by learning-based neural models in the LiDAR Point Cloud Compression (LPCC) task, realizing real-time compression - an indispensable criterion for numerous industrial applications - remains a formidable challenge. This paper proposes RENO, the first real-time neural codec for 3D LiDAR point clouds, achieving

Ruchika Sharma, Rudresh Dwivedi

Deepfake is a widely used technology employed in recent years to create pernicious content such as fake news, movies, and rumors by altering and substituting facial information from various sources. Given the ongoing evolution of deepfakes investigation of continuous identification and prevention is crucial. Due to recent technological advancements in AI (Ar